Diagnostic test kits and methods of analyzing the same

ABSTRACT

A diagnostic test kit is provided. In one aspect, the diagnostic test kit includes a diagnostic test including a test region for indicating a test result. The diagnostic test kit also includes a scan surface including one or more control markings. The one or more control markings are representative of one or more predetermined test results for the diagnostic test. The diagnostic test can include a lateral flow immunoassay test, and the one or more control markings can include one or more lines. The one or more lines can vary in at least one of thickness, color, hue, and reflectivity.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of PCT Application No.PCT/US2021/025789, filed Apr. 5, 2021, which claims priority to U.S.Provisional Application No. 63/079,975, filed Sep. 17, 2020, each ofwhich is hereby incorporated by reference in its entirety.

TECHNICAL FIELD

This invention relates generally to the field of diagnostic testingusing computer vision-aided analysis.

BACKGROUND

Medical diagnostic testing is an important component of medical care.Many diagnostic tests incorporate immunoassay tests to confirm thepresence or absence of a target analyte such as a biomarker or pathogenin a patient sample (e.g., urine, blood, saliva, sample from nasal swab,etc.). For example, one type of diagnostic test is a lateral flowimmunoassay test, in which the sample is placed onto a conjugate pad orinto the well of a cassette and liquid runs through a lateral flowimmunoassay, which then may produce a fiducial in view of a positivechemical response to the presence of a target analyte. As anotherexample, colorimetric diagnostic tests use reagents that undergo anapparent color change in the presence of a target analyte. However,interpretation of diagnostic test results using the naked eye may bechallenging and/or subjective (e.g., in cases with very faint positivetest results), which may lead to inaccurate test results.

While current diagnostic test reading devices and other clinicalanalyzers exist, they require customized reading equipment in order tostandardize the test reading environment. Many assay reader instrumentsalso require specialized training to operate. Accordingly, currentdiagnostic test reading devices require operation within a clinic,hospital, or other controlled setting so that an accurate reading may bemade. Such restrictions lead to drawbacks including patientinconvenience, increased medical care costs, and limitations inwidespread diagnostic testing. Accordingly, there is a need for new andimproved systems and methods for diagnostic testing.

SUMMARY

In some variations, a method for analyzing a diagnostic test mayinclude, at one or more processors, receiving an image depicting adiagnostic test, where the diagnostic test comprises a test regionindicating a test result, validating the quality of the image, locatinga test region image portion of the image depicting the test region ofthe diagnostic test; and predicting the test result based on the testregion image portion.

Furthermore, in some variations, a method for facilitating analysis of adiagnostic test may include, at one or more processors, receiving one ormore images depicting one or more control markings on a scan surface,where the one or more control markings are representative of one or morepredetermined test results for the diagnostic test, and verifyingdetection of the one or more control markings in the one or more imagesusing a least one computer vision technique.

In some variations, a system for facilitating analysis of a diagnostictest may include a scan surface comprising one or more control markings,where the one or more control markings are representative of one or morepredetermined test results for the diagnostic test. The scan surfacemay, in some variations, further include a test placement guideindicating placement of the diagnostic test. The scan surface may, forexample, be used as a background against which the diagnostic test maybe imaged for analyzing diagnostic test results using one or morecomputer vision techniques.

Generally, in some variations, a diagnostic test kit may include adiagnostic test comprising a test region for indicating a test result,and a scan surface comprising one or more control markings, where theone or more control markings are representative of one or morepredetermined test results for the diagnostic test.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee.

FIG. 1 is an illustrative schematic of a diagnostic platform foranalyzing diagnostic tests.

FIG. 2A is an illustrative schematic of an example variation of adiagnostic test kit for aiding analysis of a diagnostic test usingcomputer vision techniques.

FIGS. 2B-2D are illustrative schematics of example variations of samplecollection instruments with a high-contrast indicator.

FIGS. 3A and 3B depict example variations of a scan surface forreceiving a diagnostic test to be imaged.

FIGS. 4A and 4B depict example variations of a housing in a diagnostictest kit.

FIGS. 4C and 4D depict perspective views of an example variation of ahousing cover for a diagnostic test kit.

FIG. 4E is an illustrative schematic of an example variation of an assaystand in a diagnostic test kit.

FIG. 5 depicts a flowchart of an example variation of a method foranalyzing a diagnostic test.

FIG. 6 depicts another flowchart of an example variation of a method foranalyzing a diagnostic test.

FIGS. 7A and 7B depict flowcharts of example variations of a method forfacilitating analysis of a diagnostic test with the use of controlmarkings.

FIGS. 8A and 8B depict example variations of scan surfaces with printedcontrols, and analysis thereof.

FIGS. 9A and 9B depict example variations of cropping an image to aregion of interest including a diagnostic test.

FIGS. 10A-10I depict an example variation of measuring and correctingorientation of a diagnostic test in an image.

FIG. 11 depicts an example variation of cropping an image to a testregion of a diagnostic test.

FIGS. 12A and 12B depict an example variation of cropping an image to atest region of a diagnostic test.

FIGS. 13A-13F are schematic illustrations of portions of an examplevariation of a method for predicting a diagnostic test result from animage of a test region of a diagnostic test.

DETAILED DESCRIPTION

Non-limiting examples of various aspects and variations of the inventionare described herein and illustrated in the accompanying drawings.

Described herein are systems and methods for analyzing diagnostic testsusing computer vision techniques. The systems and methods may, forexample, be used to analyze results of rapid diagnostic tests thatprovide a visual indication of test results (e.g., line, color change,etc.) due to the presence of a certain chemical response associated witha medical condition. The systems and methods described herein utilizecomputer vision-based techniques to automatically interpret results of adiagnostic test using computer vision techniques that enable easy,accurate, and reliable performance of the diagnostic test from a varietyof settings, including in the home or outside of traditional healthcaresettings.

Generally, as shown in FIG. 1 , a diagnostic platform 100 may be used toanalyze diagnostic tests associated with one or more users 110. Eachuser 110 may initiate and perform a diagnostic test (e.g., by applying asample such as urine, saliva and buffer, nasal swab and buffer, or bloodand buffer to the diagnostic test), then obtain at least one image ofthe diagnostic test such as with a mobile computing device 114 having atleast one image sensor (e.g., smartphone, tablet, etc.). The mobilecomputing device 114 may communicate the image of the diagnostic testvia a network 120 (e.g., cellular network, Internet, etc.) to apredictive analysis system 130 which may include one or more processorsconfigured to utilize computer vision techniques to interpret testresults from the image of the diagnostic test. Additionally oralternatively, at least a portion of the predictive analysis system 130may be hosted locally on the mobile computing device 114. In somevariations, the mobile computing device 114 may execute a mobileapplication which may provide a graphical user interface (GUI) to guidea user through obtaining and/or applying a sample to the diagnostictest, and/or guide a user through obtaining a suitable image of thediagnostic test for analysis.

Example techniques for analyzing the image(s) of the diagnostic test aredescribed in further detail below. For example, the predictive analysissystem 130 may utilize one or more features in a diagnostic test kitthat support the computer vision-based interpretation of diagnostictests, as further described below. The predicted test results may thenbe communicated to the user (e.g., via the mobile computing device 114,such as through a GUI on an associated mobile application), to anothersuitable user (e.g., medical care practitioner), to an electronic healthrecord 140 associated with the user, other storage device(s), and/orother suitable entity.

Accordingly, the systems and methods described herein may enablediagnostic information to be quickly and easily obtained andcommunicated to provide insight on the medical condition of a user,which may in turn prompt suitable follow-up actions for medical caresuch as prescribing medication, providing medical guidance or treatment,etc. Furthermore, use of the computer vision-based techniques fordiagnostic test analysis leads to greater accuracy in testinterpretation compared to other current automated techniques such astemplate matching, which is unreliable and excessively sensitive toenvironmental factors such as lighting, type of imaging sensor, etc.Additionally, as further described below, the systems and methodsdescribed herein may advantageously be used to analyze a wide variety ofdiagnostic tests without requiring expensive, specialized hardwaredevices for analysis, nor any fiducials or landmarks on the diagnostictest itself.

Although the systems and methods are primarily described herein withrespect to analysis of medical diagnostic tests, it should be understoodthat in some variations, the systems and methods may be used in otherapplications outside of healthcare, such as in analysis of testing offood, drink, environmental conditions, etc.

Diagnostic Test Kits

As described in further detail below, a diagnostic test kit may includeone or more components for aiding analysis of a diagnostic test usingcomputer vision techniques. In some variations, a diagnostic test kitmay be configured for use with a separate (e.g., third party or off theshelf) diagnostic test. For example, a diagnostic test kit may includecomponents configured to aid a certain type or category of diagnostictest (e.g., lateral flow immunoassay test, colorimetric dipstick test,colorimetric isothermal amplification test, or lateral flow isothermalamplification test, etc.), but omit or be packaged separately from sucha diagnostic test. However, in some variations, a diagnostic test kitmay include both image analysis aid(s) and one or more diagnostic tests.In other words, a diagnostic test kit may include component(s) foraiding analysis of a diagnostic test that are packaged with or otherwisesupplied in conjunction with one or more suitable diagnostic tests.

FIG. 2A depicts a schematic of an example variation of a diagnostic testkit 200 for facilitating computer vision-aided analysis of a diagnostictest. As shown in FIG. 2 , the kit 200 may include at least one scansurface 220 that includes one or more various features, such as a testplacement guide 222 indicating placement of a diagnostic test forimaging, and/or one or more control markings 224 that are representativeof one or more possible (e.g., predetermined) test results for thediagnostic test. The test placement guide 222 and the control markings224 may be on the same surface or on separate surfaces, as furtherdescribed below. In some variations, the scan surface 220 mayadditionally or alternatively include other markings that function asfiducials for spatial referencing, color calibration, etc. Furthermore,the diagnostic test kit 200 may include one or more diagnostic tests 210for use with the scan surface 220, such that the diagnostic test(s) 210and the scan surface 220 are packaged or otherwise provided together.Alternatively, the diagnostic test kit 200 may omit a diagnostic test210, such that the diagnostic test kit 200 may be provided as asupplementary aid to a separate diagnostic test 210, to provide supportfor computer vision-aided analysis thereof.

Diagnostic Test

The diagnostic test kit 200 may include (or may be configured to supportanalysis of) one or more suitable diagnostic tests 210 (e.g., rapiddiagnostic tests). Suitable diagnostic tests include rapid diagnostictests that depict a visual indication of a test result, such as a line,color change, or other fiducial. Example types of diagnostic testsinclude lateral flow immunoassay tests and colorimetric diagnostic tests(e.g., direct flow immunoassay tests, isothermal amplification testswith a paper readout, isothermal amplification tests with a colorimetricreadout, etc.). For example, a lateral flow immunoassay test may includea test strip housed in a cassette with a window that frames a testregion of the test strip, and in the event of a positive test result, atest result line (in combination with a control line) may be visiblewithin the window of the cassette.

In some variations, the diagnostic test may include a high contrastmaterial around a test region that includes test results. The highcontrast may help enable more accurate identification of the outline orboundary of the test region using computer vision techniques. Forexample, while conventional diagnostic tests include a white cassetteenclosure and a white test strip, the performance of computer visiontechniques such as those described herein may be enhanced if thecassette enclosure is dark (e.g., gray or black) and the test strip iswhite. Accordingly, in some variations the cassette enclosure of thediagnostic test may be darker than the test strip (e.g., the cassetteenclosure may be gray or black, and the test strip may be white).However, other high contrast cassette colors (e.g., bright green, brightpurple) that contrast with the test strip color (and/or a scan surfacesuch as that described below) may also be suitable. Additionally oralternatively, in some variations, the cassette material may be lessreflective than the test strip. For example, even a plastic cassettematerial that is slightly less reflective than the test strip materialmay greatly enhance the ease of image segmentation such that the exactboundaries of the test strip can be easily located. In some variations,the cassette of the diagnostic test may be plastic and a light graycolor, which may offer sufficient contrast with the test strip for highaccuracy segmentation of the image in a wide range of lightingconditions.

Additionally or alternatively, the diagnostic test may include one ormore geometrical features to enhance performance of the computer visiontechniques described herein. For example, in some variations the assaywindow in the diagnostic test may have beveled or rounded edges, whichmay reduce the effect of shadows cast upon the test strip.

The diagnostic test 210, for example, be configured to receive a samplefrom a user such as blood, plasma, serum, urine, saliva, solubilizedsolids, and/or a substance from a nasal swab, which may be analyzed toassess a medical condition of the user. The diagnostic test 210 may beconfigured to test for a medical condition such as viral infection(e.g., influenza, hepatitis, zika virus, dengue fever, chikingunya,norovirus, coronavirus (e.g., COVID-19)), bacterial infection,parasite-caused diseases (e.g., malaria), pregnancy and/or any suitablemedical condition (e.g., chronic kidney disease, porphyria,hyperoxaluria, dehydration, autoimmune conditions, inflammatorydiseases, drug abuse, allergic reactions, hypercholesterolemia, orhypertriglyceridemia, etc.). In some variations, the diagnostic test 210may include multiple test regions (e.g., multiple assay windows) tofacilitate simultaneously testing for multiple medical conditions (e.g.,influenza and coronavirus). The diagnostic test 210 may include, forexample, two, three, four, or five or more test regions.

Sample Collection Tools

In some variations, the diagnostic test kit 200 may further include oneor more sample collection tools to facilitate collection of a samplefrom a user. Suitable sample collection tools include, for example,nasal swabs, oral collection swabs, saliva collection containers, cups,tubes, etc. The sample collection tools may be configured to enable oneor more computer vision techniques to track location and movement of thesample collection tool in order to verify, in a video of a usercollecting a sample with one or more of the sample collection tools,whether a sample has been collected correctly.

For example, one or more sample collection tools may include a highcontrast indicator having a high contrast color (e.g., bright green,bright purple). Additionally, or alternatively, the high contrastindicator may include another visually striking characteristic, such asfluorescence or high reflectivity. Even further, in some variations thehigh contrast indicator may include a computer-readable fiducial, suchas an ArUco marker, WR code marker, etc. The high contrast indicator maybe integrated in the sample collection tool (e.g., as a dye or coatingon a portion or all of the sample collection tool), and/or may include aseparate component coupled to the sample collection tool.

FIGS. 2B-2D are a schematic depictions of example variations of highcontrast indicators. As shown in FIG. 2B, a high contrast indicator 230a may include a ring or sleeve that telescopically engages a memberportion of a sample collection tool, such as the shaft of a swab (asshown in FIG. 2B) or body of a cup or tube. Alternatively, the highcontrast indicator 230 a may include tape that is attached around thesurface of the sample collection tool. The high contrast indicator 230 amay be coupled to the sample collection tool with one or more fasteners(e.g., adhesive) or through a mechanical interfit (e.g., interferencefit), or included in the manufacture of the collection tool as a dye forthe entire tool or dye for a specific region or component of the tool,etc. The indicator may be high contrast along any one or more regions ofthe electromagnetic spectrum including visible light/color, infrared,ultraviolet, etc. As an illustrative use example, the high contrastindicator 230 a may be engaged on the shaft of a nasal swab proximal tothe distal flocking that is inserted into the nasal cavity of a user.The high contrast indicator 230 a is highly visible in video and may betracked to enable determination of whether the swab was inserted intothe nostril or oral cavity of the user to the correct depth. In somevariations, multiple high contrast indicators of the same or differenthigh contrast features may be arranged along the length of thecollection swab or other sample collection tool. Multiple such highcontrast indicators may, for example, be used to determine depth of swabinsertion in a video of the user collecting the same, and/or provideguidance to the user to improve sample collection technique (e.g.,instruct the user to insert the swab deeper).

In some variations, a functional component of the sample collection toolmay include a high contrast indicator. For example, as shown in FIG. 2C,a sample collection vessel (e.g., cup, tube) may include a high contrastindicator 230 b in the form of (or coupled to) a vessel cap.Additionally or alternatively, in some variations, a high contrastindicator may include a sticker that is coupled to a surface of thesample collection tool, or included in the manufacture of the collectiontool as a dye for the entire tool or dye for a specific region orcomponent of the tool, etc. The indicator may be high contrast along anyone or more regions of the electromagnetic spectrum including visiblelight/color, infrared, ultraviolet, etc. For example, as shown in FIG.2D, a sample collection vessel (e.g., cup, tube) may include a highcontrast indicator 230 c sticker that is applied to a surface of thesample collection vessel. Such high contrast indicators may be trackedsimilar to that described above, and their positions and/or orientationsmay be analyzed in order to determine whether a user has obtained asample with a correct procedure.

In some variations, motion of the sample collection tool may be trackedwith respect to the user's face (which may be detected using suitablefacial recognition approaches) and/or another body part of the user(e.g., finger, hand, arm, etc.) to determine whether the user hasperformed a sample collection procedure appropriately. Additionally oralternatively, shape recognition and/or tracking may be performed usinga depth camera (e.g., infrared camera with 3D depth mapping) and/orother suitable sensors (e.g., proximity sensors) to identify and/ortrack a sample collection tool, to similarly determine whether a userhas performed a sample collection procedure appropriately.

Scan Surface

As described above, the diagnostic test kit 200 may include one or morescan surfaces 220 which may be placed behind any suitable diagnostictest as an aid for computer vision-based analysis. The scan surface may,for example, be located on a card, tray, mat, pedestal, housing,instruction booklet, or any suitable physical structure configured toreceive a diagnostic test. The scan surface may be formed on paper,plastic, cardboard, or other suitable material.

As shown in FIG. 2A, one or more scan surfaces 220 may include a testplacement guide 222 indicating placement of a diagnostic test againstthe scan surface for imaging, and/or one or more control markings 224that are representative of one or more predetermined test results forthe diagnostic test. Various other fiducials may additionally oralternatively be included on the one or more scan surfaces to aidcomputer vision techniques, as further described below.

Any of the visual features on the scan surface (e.g., test placementguide, spatial markers, calibration markers, control markings, otherfiducials, etc.) may be printed or otherwise applied directly onto thescan surface or on a decal that is applied to the scan surface. Forexample, the visual features may be printed in ink (e.g., color ink,black ink, fluorescent ink, etc.), paint, and/or laser jet toner, etc.In variations in which some or all visual features are printed influorescent ink, the fluorescent ink may include, for example, an inkincluding europium, rhodamine, fluorescein, alexa fluor, quantum dots,and/or fluorescent nanoparticles. Printing visual features on the scansurface with fluorescent inks may, for example, enable the diagnostictesting kit to be compatible with diagnostic assays that use afluorescent readout mechanism (e.g., products with fluorescent particlesor dyes, which require a specialized reader instrument). In somevariations, the visual features may be printed in a digital printingprocess, a plate printing process, and/or other suitable printingprocess.

Test Placement Guides

In some variations, a scan surface may include a test placement guidethat provides an indication of where a user should place a diagnostictest in order to be imaged and analyzed. The test placement guide mayinclude one or more features to aid automated analysis of the diagnostictest.

As shown in the schematic of FIG. 2A, a test placement guide 222 mayinclude a background color that is high contrast relative to thediagnostic test to be imaged. The high contrast background may helpensure that the outline of the diagnostic test may be reliably detectedby computer vision techniques. For example, many diagnostic tests arewhite (e.g., in a white cassette housing). Accordingly, for these and/orother light-colored diagnostic tests, the test placement guide 222 mayinclude a dark region (e.g., black or dark gray) or brightly coloredregion (e.g., bright green or bright purple, etc.) that stronglycontrasts with the diagnostic test.

The bounded area of the contrasting background may be larger than thebounded area of the diagnostic test intended to be imaged (e.g.,providing a margin of contrasting background that is at least 0.1 cm,between about 0.1 cm and about 5 cm, between about 0.1 cm and about 1cm, between about 1 cm and about 2 cm, between about 2 cm and about 5cm, or any other suitable margin, or a certain percentage of the higherof either the test length or width such as 10%, 20%, 50%, or any othersuitable margin). However, the background area may be limited so as tonot impact the ISO/exposure time adjustment of the image sensor (e.g.,too much black in a background can cause some image sensors toovercompensate by adjusting ISO so high that it results in saturation ofthe white color in some sections of the imaged diagnostic test). When animaged diagnostic test is placed onto a high contrast background, theoutline of the diagnostic test may be identified in the image throughtechniques such as contour detection. In this manner, the outline of anydiagnostic test that fits within test placement guide 222 may bedetermined. In other words, the diagnostic test determination may beperformed independent of any custom marking or other fiducials on thediagnostic test itself. Accordingly, the high contrast background of thescan surface may advantageously enable the diagnostic test kit to bemore versatile, so as to support a greater variety of diagnostic tests.

Furthermore, in some variations the test placement guide 222 may includeother markings and/or other features to instruct placement of adiagnostic test on the guide 222. For example, in some variations thetest placement guide 222 may include text (e.g., “Place test here”), agraphical representation of a diagnostic test (e.g., line drawing) orboundary thereof, and/or suitable symbols (e.g., arrows) to suggestproper position and/or orientation of a diagnostic test against the scansurface. Such additional guidance may, for example, be visual (e.g.,printed directly on the scan surface, printed on a decal affixed to thescan surface, etc.) and/or textural (e.g., indentations, raisedfeatures, etc.).

An example variation of a scan surface 300 (e.g., scan card) is shown inFIG. 3A. As shown in FIG. 3A, the scan surface 300 includes a testplacement guide 310 that includes a dark-colored background configuredto be high contrast relative to a light-colored diagnostic test to beplaced on the test placement guide 310. The test placement guide 310also includes instructive text (“Place cassette here”) and a graphicalrepresentation of a lateral flow assay test cassette to suggest properorientation of the cassette relative to the scan surface. For example,the graphical representation shown in FIG. 3A includes an outline of acassette with its sample intake (pictured as a short line) directedtoward the lower edge of the scan surface 300, and its test region(pictured as a longer line) directed toward the upper edge of the scansurface 300. Accordingly, the test placement guide 310 includes asuggestion to orient the test cassette in a manner similar to diagnostictest (T) shown in FIG. 3B.

Spatial Markers

In some variations, the scan surface may include one or more spatialmarkers that function to help facilitate spatial locating and/oridentification of the spatial orientation of the diagnostic test and/ora test region (a region of the test displaying test results) within theimage. In some variations, the spatial markers may be located in and/oraround the test placement guide (that is, a region of the scan surfaceexpected to receive a diagnostic test) in an arrangement that defines aboundary of the diagnostic test in the image. By identifying the spatialmarkers, this boundary around the diagnostic test may be identified,thereby enabling cropping of image to isolate the diagnostic test forfurther analysis without interference from the background of the image.For example, the scan surface may include at least three spatial markersthat form vertices of a bounded area. Generally, the spatial markers mayinclude any suitable fiducial, such as ArUco markers, QR code markers,other computer-readable markers, or custom markers with sufficientlycontrasting visual characteristics. Additional details of use of spatialmarkers during image analysis are described in further detail below.

As shown in the example depicted in FIG. 3A, the scan surface 300 mayinclude four spatial markers 320 that arranged at corners of a boundedrectangular area around the test placement guide 310. In somevariations, three of such spatial markers may be sufficient to definethe bounded rectangular area by defining a width and a length of therectangular area. Although the scan surface 300 is depicted in FIG. 3Aas including four spatial markers forming a rectangular boundary thatprovides guidance for a rough image crop, it should be understood thatin other variations, the scan surface may include any suitable number ofspatial markers forming any suitable shape (e.g., three markers, fivemarkers, six markers, etc.). Furthermore, while the scan surface 300shown in FIG. 3A includes ArUco markers functioning as spatial markers,it should be understood that in other variations the spatial markers mayhave any suitable form. For example, FIG. 3B depicts an examplevariation of a scan surface 302 that is similar to the scan surface 300depicted in FIG. 3A, except that the spatial markers in the scan surface302 are QR markers instead of ArUco markers.

Calibration Markers

In some variations, the scan surface may include other suitable markersfor calibration or other reference. For example, the scan surface mayinclude standard color and/or grayscale markings that may function as areference for automatic color correction (e.g., white balance) by animage sensor, so as to reduce the influence of illuminant conditionsthat may interfere with accurate test result interpretation.Additionally or alternatively, such color calibration markers may appearon any suitable surface, such as a surface that is separate from thescan surface for receiving the diagnostic test. For example, colorcalibration markers may be present on a separate calibration card thatmay be referenced separately and prior to imaging the diagnostic test onthe scan surface.

Additionally or alternatively, the scan surface may include alignmentmarkers that may function to indicate a predetermined geometry andorientation of the diagnostic test on the assay. Such alignment markersmay, for example, be similar to markings on the test placement guide 222as described above and/or as shown in FIG. 3A as described above.

Control Markings

As shown in FIG. 2A, in some variations, the scan surface 220 mayinclude one or more control markings 224 that are representative of oneor more predetermined test results for the diagnostic test. For example,the control markings may include shapes having a specific color,thickness, and/or intensity that correspond to the expected shape,color, thickness, and/or intensity of the test results and/or testcontrol markings in the diagnostic test. In some variations, the controlmarkings 224 may include at least one marking that corresponds to thelower limit (or near the lower limit) of detection or quantitation ofthe diagnostic test.

The control markings 224 may function to help ensure that the camera isable to generate an image of adequate quality that permits detection ofa fiducial indicating a test result, including a faintly visiblefiducial. For example, the control markings 224 may be used to helpensure that the camera has sufficient resolution, sufficient imagesensor quality, and/or sufficient autofocus, autoexposure, and/orcolor/white balancing settings and/or abilities. If each of the one ormore control markings 224 is detected in an image of the controlmarkings by the diagnostic test platform's computer vision techniques,it is likely that the platform is capable of correctly interpreting thetest result of the diagnostic test, including positive and faintpositive results.

Appearance of the control markings may vary depending on the type ofdiagnostic test with which the control markings are associated with. Forexample, for a diagnostic test such as a lateral flow immunoassay test,the control markings 224 may include a set of lines. As shown in theschematic of FIG. 2A, the lines may vary in thickness, color,reflectivity, and/or hue (e.g., darkness), and at least some of thelines may be similar in size, shape, and/or color to the expectedcontrol lines and/or test result lines that appear on the lateral flowimmunoassay test. For example, in some variations the printed controlsmay include black or gray lines (e.g., of varying intensity ingrayscale). As another example, in some variations the printed controlsmay include lines of varying colors (e.g., red, blue, green, etc.). Ifeach of the lines is detected by the computer vision techniques, it islikely that the computer vision techniques are able to correctlyinterpret test results in images of the diagnostic test.

As another example, for a diagnostic test such as a colorimetricimmunoassay test, the one or more control markings may include a set ofcolored markings. As shown in FIG. 4B, for example, a scan surface 420may include a series of control markings 424 including colored boxes.Similar to that described above, the color of each box may berepresentative of a predetermined test result for the diagnostic test(e.g., positive or negative test, positive control, negative control,etc.). Additionally or alternatively, the control markings may includeblack or gray boxes (e.g., of varying intensity in grayscale). If eachof the colored, black, and/or gray boxes is detected by the computervision techniques, it is likely that the computer vision techniques areable to correctly interpret test results in images of the diagnostictest.

Although in some variations, as shown in FIGS. 2A and 4B, the controlmarkings may include geometric shapes arranged in an array (e.g., 1Darray, 2D array), it should be understood that the control markings mayincorporate the appropriate control markings as part of any suitabledesign. For example, the control markings may be artfully arranged in asuitable graphical design (e.g., graphical icons such as a wave, house,tree, simple lines, etc.), which may contribute to a more aestheticallypleasing design of the diagnostic testing kit.

Similar to other visual features of the scan surface as described above,the control markings may be printed onto a card, paper, mat, tray,housing (e.g., box), and/or other suitable surface. In some variations,the control markings may be located proximate (e.g., adjacent) a testplacement guide on the scan surface, such that the diagnostic test andthe control markings may be in the same field of view of a camera (to beimaged together) imaging the diagnostic test. In some variations, someor all control markings may be located on a separate component than thecomponent that receives the diagnostic test, as the control markings maybe imaged separately from (e.g., prior to) imaging the diagnostic test.Analysis of the control markings prior to imaging the diagnostic testmay, for example, help enable a user to determine whether a specificcamera device is adequate prior to consuming a diagnostic test, therebyavoiding waste of a diagnostic test in the event that the user'sintended camera device is not able to obtain an image of adequatequality for analysis. Other details of the use of control markings toverify camera quality are described further below.

Encoded Information

In some variations, the diagnostic test kit may include one or morecomputer-readable codes for easy and reliable identification and/ortraceability of the test kit and/or its components. For example, thediagnostic test kit may include a QR code, a bar code, and/or othersuitable markings that encode information associated with the diagnostictest kit, such as expiration date, product SKU, lot number, and/or thelike. Additionally or alternatively, the computer-readable code mayencode routing information that directs communication software (e.g., ina mobile application executed on a mobile computing device) to transmittest results and/or any associated metadata (e.g., name, date/time oftest, information relating to calibration or image quality control,etc.) to a particular designated destination, such as a specific server,cloud service, email address, mobile number, etc.

In some variations, one or more such computer-readable codes may beprinted or otherwise located on a scan surface, and may be proximate toa test placement guide so as to be in the same field of view as thediagnostic test and/or other markings on the scan surface.Alternatively, one or more computer-readable codes may be on a separatescan surface (e.g., on a housing as described below, on a separateinstruction booklet, etc.).

Color References

Additionally or alternatively, in some variations the scan surface mayinclude a color reference array or other pattern that includes examplecolors for use in analyzing test results of a colorimetric diagnostictest. For example, a color reference array may include a group ofcolored blocks or other icons, each of which may correspond to aparticular test result, and may be intended for assessing color of anassay with a particular test result (e.g., analyte concentration). Thecolor reference array may include colored icons arranged in a grid, anartful arrangement, or any suitable pattern. In some variations, thecontrol markings as described above may additionally function as a colorreference array for purposes of test analysis. Additionally oralternatively, the color reference array may be similar to thosedescribed in U.S. Pat. Nos. 8,655,009 and 8,911,679, each of which isincorporated herein by this reference.

Housing

In some variations, the diagnostic test kit may include a housing. Forexample, as shown in FIG. 4A, in some variations a diagnostic test kitmay include a housing 400 that includes at least one compartment 430that may receive one or more diagnostic test kit components. In somevariations, the housing may be useful for diagnostic tests that lack aplastic enclosure or cassette. The compartment 430 may, for example, beconfigured to store components such as one or more tools for samplecollection and/or sample manipulation (e.g., swabs, cups, tubes,centrifuge tubes, capillary tubes, disposable pipette, other samplevessels, test strips, syringes, needles, syringe filters, alcohol wipes,reagents, etc.). In some variations, components may be organized intrays (e.g., injection molded trays with cavities shaped to correspondto components), boxes, and/or in any suitable manner in the compartments430. The components may be removed for use during the testing process.

Additionally or alternatively, in some variations the housing mayinclude a scan surface 420 that is similar to scan surfaces describedabove, and the housing may function as an assay stand. For example, asshown in FIGS. 4A and 4B, the scan surface 420 may include a highcontrast background, spatial markers 426, and/or other suitablecalibration markers that help computer vision techniques to locate adiagnostic test that is placed against the scan surface 420.Furthermore, as shown in FIG. 4B, the scan surface 420 may include oneor more control markers 424, as described above. In some variations, asshown in FIG. 4B, the scan surface 420 may be sloped or angled upward(e.g., due to geometry of the housing, orientation of the housing with akickstand such as that shown in FIG. 4E for an assay stand, etc.), whichmay improve illumination of the diagnostic test on the scan surface 420,avoid shadows, etc. In some variations, the angle may be fixed, while insome variations the angle may be adjustable, such as with an adjustablekickstand.

However, the scan surface 420 shown in FIGS. 4A and 4B may include atest placement guide 422 including at least one receptacle (e.g., cutoutor indentation) configured to receive a diagnostic test for analysis.For example the receptacle may be shaped and sized to cradle adiagnostic test (e.g., cassette, sample vessel, etc.). Furthermore, insome variation the receptacle may share a wall (or membrane, etc.) withthe compartment 430 such that in variations in which a heating device isarranged in the compartment 430 (as described in further detail below),heat may transfer from the heating device across the shared wall to adiagnostic test positioned in test placement guide 422. The diagnostictest may be secured in the receptacle via a mechanical interfit such asa snap fit, adhesive, etc. such that it is held in place on the scansurface 420. The test placement guide 422 may include a singlereceptacle as shown in FIG. 4A, or multiple receptacles as shown in FIG.4B. For example, the test placement guide 422 shown in FIG. 4B mayinclude three receptacles for receiving vessels providing a positivecontrol, a negative control, and a test result. However, it should beunderstood that in other variations the test placement guide 422 mayinclude any suitable number of receptacles of any suitable shape (e.g.,for receiving conical sample tubes, pipettes, capillary tubes, cuvette,etc.). Additionally or alternatively, other cutouts (e.g., notches 436)or other suitable mounting features may formed in the side or othersuitable surface of the housing, so as to provide a mount point forcomponents such as a cuvette holder, etc.

The housing may, in some variations, include a cover for covering thecompartment and/or enclosing the housing. For example, FIGS. 4C and 4Ddepict an example variation of a housing cover 440 that may couple to ahousing such as housing 402 depicted in FIG. 4B. The housing cover 440may include one or more alignment features 446 that are configured toengage with alignment features 434 on the housing 402, which may helporient and/or secure the housing cover 440 on the housing 402. In somevariations, the housing cover 440 may include one or more receptacles442 for receiving diagnostic test components (e.g., conical tubes forreaction mixtures, etc.). The location of these receptacles 442 may, forexample, be advantageous in variations in which heat from a heatingdevice (as described in further detail below) in the compartment 430 isused to warm items in the receptacles 442. In some variations, thereceptacles 442 may be used to warm sample vessels (e.g., positivecontrol, negative control, test result) for a predetermined period oftime (e.g., 30 seconds), and then the warmed vessels may be transferredto an imaging area such as the scan surface 420.

The housing may be made in any various suitable manners. For example,the housing may be injection molded, 3D printed, milled, folded, and/orformed in any suitable process. The housing may include any suitablematerials, such as plastic, paper (e.g., wax paper), cardboard, metal,etc.

Heating Device

In some variations, the diagnostic test kit may include a heatingdevice. Many diagnostic tests utilize an isothermal amplification step,for which the heating device may provide heat. As shown schematically inFIG. 4A, the heating device 432 may be located in the compartment 430 ofthe housing, so as to warm items placed in one or more receptacles inand/or around the housing. For example, when placed in the compartment430, the heating device may be configured to warm diagnostic test(s)(including test samples, controls, etc.) on a scan surface 420 and/orhousing cover 440. However, one or more heating devices may be arrangedin any suitable location (e.g., on the exterior of the housing, such ason a front surface, back surface, bottom surface, side surface, etc.).Even further, in some variations the heating device may be separate fromthe housing, such as in a standalone structure configured for warmingtest samples.

In some variations in which the heating device 432 is in the compartment430 of the housing, the housing may include features to enhance heattransfer between the heating device 432 and the diagnostic device and/orother vessels for warming. For example, the housing may include channelsor cavities configured to maximize surface area contact between theheating device and the diagnostic device. As another example, thehousing may additionally or alternatively include thermally conductivematerial (e.g., aluminum or other conductive material) to function asconduits of heat between the heating device and desired locations forwarming.

The heating device may include any suitable kind of heating mechanism.For example, the heating device may include a water-activated quicklimeheater, an air-activated heater (e.g., with cellulose, iron, activatedcarbon, or other suitable substance that produces heat from anexothermic reaction upon exposure to air), electrical heater, or otherchemical heater. In some variations, the heating device may reachtemperatures over about 65° C. for use with LAMP, rolling circleamplification, NEAR, tadpole and other isothermal amplificationreactions. The heating device may additionally or alternatively be usedto perform cell lysis and/or other heating steps critical for somediagnostics tests.

Other Imaging Aids Assay Stand

In some variations, a diagnostic test kit may additionally oralternatively include a stand configured to orient the scan surface andthe diagnostic test at a suitable angle to improve illumination of thediagnostic test and achieve a better image of the diagnostic test. Theassay stand may be similar to the angled housing 402 described abovewith respect to FIG. 4B, except that the assay stand may omit a housingcompartment. In some variations, the stand may be useful for diagnostictests that lack a plastic enclosure or cassette. As shown in theillustrative schematic of FIG. 4E, the assay stand 450 may, for example,include a tray or other holder 352 that may include a scan surfacesimilar to that described above (e.g., with alignment markers) forreceiving a diagnostic test (T). The stand may be configured to anglethe diagnostic test upwards and/or reflect ambient light upward towardthe assay in order to improve the homogeneity of lighting on thediagnostic test. For example, the stand may be configured to angle thediagnostic test at an angle between about 10° and about 80°, betweenabout 25° and about 65°, between about 35° and about 55°, or about 45°,etc. The scan surface may be angled due to an angled face of the stand,and/or due to an angle of the stand relative to a level surface. Theangle may be fixed such as with a static angled bracket or otherconstruction. Alternatively, the angle may be adjustable such as with anangled kickstand (e.g. as kickstand 454 shown in FIG. 4E).

Fluorescence-Related Accessories

In some variations, a diagnostic test kit may includefluorescence-related accessories to assist in imaging. For example, adiagnostic test kit may include accessories to be compatible withdiagnostic tests that use a fluorescent readout mechanism (e.g.,products with fluorescent particles or dyes). In some variations, adiagnostic test kit may include one or more excitation light sources(e.g., ultraviolet (UV) light sources) for exciting fluorescentparticles or dyes in a diagnostic test. Additionally or alternatively, adiagnostic test kit may include one or more suitable UV filters, to beplaced between the diagnostic test and the camera during imaging, tofacilitate the imaging of fluorescent light emitted from the diagnostictest kit, for test analysis purposes.

It should be understood that variations of the diagnostic test kits mayinclude any suitable combination of the components described herein.Furthermore, in some variations certain components of the test kit maybe reusable (e.g., assay stand, fluorescence-related accessories) and berepeatedly used with multiple disposable components (e.g., multiplediagnostic tests). Alternatively, in some variations all components ofthe diagnostic test kit may be designated for single use.

Methods of Analyzing a Diagnostic Test

As shown in FIG. 5 , a method 500 for analyzing a diagnostic test mayinclude, at one or more processors, receiving an image depicting adiagnostic test 510 where the diagnostic test includes a test regionindicating a test result, validating quality of the image 520, locatinga test region image portion of the image 530 depicting the test regionof the diagnostic test, and predicting the test result based on the testregion image portion 540. Furthermore, in some variations, the method500 may further include communicating the predicted test result 550 to auser or other entity, and/or storing the predicted test result 560(e.g., in a user's electronic health record, in a user accountassociated with the diagnostic platform, etc.). In some variations, themethod may include verifying sample collection 502 (before, during, orafter image analysis to determine test result), using techniques such asthat described above with references to FIGS. 2B-2D. Furthermore, insome variations the method may include verifying detection of one ormore control markings on a scan surface 504, which may, for example, beused to assess camera quality used to obtain an image for diagnostictest analysis.

In some variations, the method 500 may be used in conjunction withdiagnostic test kits such as those described above (or componentsthereof). The method 500 may be performed locally such as on a mobilecomputing device (e.g., mobile application executed on the computingdevice and is associated with the diagnostic platform), and/or remotelysuch as on a server (e.g., cloud server).

Verifying Detection of Control Markings

In some variations, the method may include assessing the quality of thecamera and/or image sensor (and/or imaging conditions) to be used forobtaining an image of the diagnostic test for analysis. For example, asshown in FIG. 7A, a method 700 for facilitating analysis of a diagnostictest may include receiving an image of a diagnostic test and one or morecontrol markings (710). Control markings may include suitable markingsthat are representative of one or more predetermined test results forthe diagnostic test, as described in further detail above. Computervision techniques may assess whether all (or a sufficient portion) ofthe control markings may be detected in the image (720). If not all ofthe control markings are detected in the image, then the user maynotified of the error (730). For example, a suggestion may be providedto the user to try a different camera, change one or more camerasettings, adjust one or more environmental factors, or any combinationthereof, etc. If the control markings are detected in the image, thenthe image may be further analyzed to predict the diagnostic test result(740), and the test result may be output or otherwise communicated (750)such as that as described below.

Although the method 700 depicted in FIG. 7A utilizes an image thatincludes both the diagnostic test and the control markings, in somevariations separate images may provide this information. For example, asshown in FIG. 7B, a method 700′ for facilitating analysis of adiagnostic test may include receiving a first image of one or morecontrol markings (710 a) and computer vision techniques may assesswhether all or a sufficient portion of the control markings may bedetected in the first image (720). If not all of the control markingsare detected in the image, then the user may be notified of the error(730) as described above. If the control markings are detected in theimage, then a user may be prompted to take a second image that depictsthe diagnostic test (710 b). The second image may then be furtheranalyzed to predict the diagnostic test result (740) and output orotherwise communicated (750).

FIG. 8A illustrates an example variation of verifying detection of oneor more control markings on a scan surface including a plurality ofprinted control markings 810. Specifically, FIG. 8A depicts an image ofa scan card having control markings 810 that include a set of nine linesof varying reflectivity. The nine lines may be considered representativeof test results in a lateral flow immunoassay ranging from faintpositive to strong positive. In analyzing the image, a bounded imageregion 820 may be identified as including the printed control markings810, based on, for example, identifying fiducials (e.g., ArUco markers)known to be on either side of the appropriate image region 820. Thefiducials may be identified with suitable computer vision techniquessuch as built-in functions of OpenCV and/or similar computer visionsoftware packages. The image region 820 may be cropped, and its pixelsmay be condensed into a 1D array of pixel values, where each element ofthe 1D array may include a representative metric (e.g., mean, median,other statistical measure, etc.) of an image segment (e.g., row orcolumn of pixels) in the image region 820. The relative values of theelements of this 1D array may be analyzed to determine a series ofpeaks, each of which may be considered to potentially correspond with arespective control marking. In some variations, white space in theregion of the control markings may be used as a negative control, inthat the system should expect no peaks to be detected in the whitespace. The resulting count of identified peaks may be compared to anaccepted predetermined value (e.g., known number of control lines). Ifthe peak count is sufficiently similar to the accepted predeterminedvalues (e.g., are at least accepted threshold values, or fall within acertain range of threshold values), then the camera used to take theimage of the control markings and/or the imaging conditions may beconsidered of sufficient quality. In the example of FIG. 8A, analysis ofthe 1D array for the image region 820 results in a count of nine peaks,which matches the predetermined, known number of control lines 810 onthe scan card. Accordingly, FIG. 8A represents a situation in which thecontrol check has passed based on peaks identified in the image region820, and subsequent image analysis may proceed.

FIG. 8B illustrates another example variation of verifying detection ofone or more control markings on a scan surface. The variation of FIG. 8Bis similar to that of FIG. 8A, except that the method further considersthe prominence of each identified peak, which represents how much thepeak stands out due to its intrinsic height and/or location relative toother peaks. The resulting count of peaks and/or their prominences maybe compared to accepted predetermined values (e.g., are at leastaccepted threshold values, or fall within a certain range of thresholdvalues). If the number of peaks and/or their prominences satisfy thepredetermined conditions, then the camera used to take the image of thecontrol markings and/or the imaging conditions may be considered ofsufficient quality. In the example of FIG. 8B, analysis of the 1D arrayfor the image region 820 results in a count of nine peaks, and measuredprominences of each peak also satisfies predetermined conditions.Accordingly, FIG. 8B represents a situation in which the control checkhas passed based on peaks and prominences identified in the image region820, and subsequent image analysis may proceed.

Furthermore, in some variations, the printed control markings mayadditionally or alternatively be assessed with other methods, such asbased on detection of each test band contour and measurement of thevalues within that contour, measurement of the values in pre-determinedspatial regions, and/or analysis by a trained machine learning model.

Receiving Images

FIG. 6 illustrates in detail various aspects of computer visiontechniques for analyzing an image of a diagnostic test. As describedabove, an image may be received (610) that depicts a diagnostic testagainst a background. The background may or may not include a designatedscan surface with fiducials. In some variations, the image may beobtained by a user with the assistance of an application executed on amobile computing device, or by a camera connected to a testing apparatuswith a computing device and a display. The application may, for example,display on-screen instructions and/or guidance (e.g., displayed reticle)for the user to obtain a suitable image. In some variations, theapplication may additionally or alternatively utilize sensors in thecomputing device or testing apparatus, such as light sensors, toautomatically adjust camera settings (e.g., white/color balance, etc.)to optimal conditions and/or provide suggestions to the user (e.g.,“Move to somewhere with more light.”), and/or close a feedback loop toautomatically increase the illumination of the test. The image may be inany suitable format, such as jpeg, png, RAW, etc.

Assuming that the control check (e.g., as described with respect toFIGS. 7A and 7B) has passed, analysis of the image may proceed, such asby generating a roughly cropped version of the image (612) around theimaged diagnostic test to isolate the diagnostic test from thebackground. In variations in which a control check is not performed,analysis of the received image may automatically proceed as furtherdescribed below.

In some variations, the rough crop (612) may be automatically performedusing computer vision techniques with the aid of a scan surface. Forexample, FIG. 9A depicts an image 910 of a diagnostic test on a scancard with spatial markers (QR code markers, though they may includeArUco markers or any suitable fiducial). The image 910 may be roughlycropped to a region of interest 920 that surrounds the diagnostic test.This region of interest 920 may be located, for example, by identifyinga set of three or more spatial markers, such as with built-in functionsof OpenCV or other suitable computer vision software packages. The areabounded by these spatial markers can be subject to further analysis.

In some variations, the rough crop (612) may be automatically performedusing additional computer vision techniques with other image analysiswith the aid of a scan surface. For example, FIG. 9B depicts an image910 of a diagnostic test on a scan card with spatial markers (QR codemarkers), where the image 910 may be cropped to a region of interest 920that surrounds the diagnostic test. Like the method described above withrespect to FIG. 9A, three of more spatial markers (e.g., QR codemarkers, though they may include ArUco markers or any suitable fiducial)may be identified, but here the spatial markers may be identified byconverting the image 910 to a different color space and identifyingcontours that indicate the location of each spatial marker. For example,the image may be converted to grayscale to make the analysiscolor-agnostic, then threshold cutoffs may be applied to helpdistinguish between the region of interest and the background. Thethreshold cutoffs may, for example, be based on heuristically-determinedthreshold that may be a function of average light intensity of targetarea vs. a “background” area, and contour detection to identifycandidate locations of spatial markers. Test of region size and/orregion aspect ratio may be applied to candidate locations of spatialmarkers to identify where the spatial markers are located in the image.In the variation shown in FIG. 9B, one of the spatial markers includes ared square in a predetermined, known location among the spatial markers(lower righthand corner). After the set of spatial markers is located,the red square is identified, and the region of interest 920 isidentified as an area of the image 910 that is a multiple of that redsquare (e.g., a region of (M times the height of the red square)×(Ntimes the width of the red square)), using a corner of the red square orother suitable point relative to the red square as an origin formultiplied coordinates.

Additionally or alternatively, a rough crop of an image may beautomatically performed by first converting the image to grayscale andapplying threshold cutoffs as described above with respect to FIG. 9B.Subsequently, one or more filters (e.g., median filters) may be used toremove shadows from the image. Open, closed, and/or gradient morphologyoperations may be performed on the image, and analysis may be focused onregions of the image that were outlined by the on-screen reticle.Min-area algorithms may be used to determine a best-fit box around thediagnostic test as the region of interest for a rough crop of the image.

Furthermore, in some variations, a rough crop (612) of the image of thediagnostic test may additionally or alternatively include manual inputto designate boundaries of the diagnostic test. For example, after auser takes an image of the diagnostic test against a background, such aswith a mobile computing device (e.g., smartphone) executing a mobileapplication, the mobile application may prompt a user to indicate aregion of interest around the imaged diagnostic test. A user mayindicate the region of interest by manually tracing a region of intereston the displayed image using a suitable graphic user interface and/orindicating vertices of a geometrical region of interest (e.g., cornersof a rectangle), for example. Alternatively, the user may crop, mark, orotherwise edit the original image of the diagnostic test using anysuitable photo editing application on a computing device, and designatethat edited photo for further analysis. As yet another example, a roughcrop of the image of the diagnostic test may additionally oralternatively be based on an on-screen reticle and/or other suitableguide displayed on a viewing screen of an imaging device. For example, aGUI of an imaging device (e.g., smartphone) may display a reticle havingthe same aspect ratio as a target (e.g., diagnostic test, test region ofthe diagnostic test, etc.), where a user may align the target with thereticle prior to capturing the image. As a result, in some variations,the rough crop may be based on a cropped region of pixels in thereceived image that corresponds to the displayed reticle.

In some variations, a rough crop (612) may incorporate both automaticdetermination and manual input relating to a region of interest in theimage of a diagnostic test. For example, any of the above-describedautomated techniques may determine a proposed region of interestdefining a rough crop around the imaged diagnostic test, and theproposed region of interest may be displayed on a screen of a computingdevice (e.g., smartphone) for confirmation and/or manual adjustment by auser.

Additionally or alternatively, a rough crop (612) of the image mayincorporate known characteristics of the diagnostic test being imaged.For example, the type (e.g., brand, etc.) of the diagnostic test may bedetermined, and one or more characteristics such as overall shape oraspect ratio of the diagnostic test may be known for that type ofdiagnostic test (e.g., in a stored configuration file). Information fromthe configuration file for that diagnostic test may be utilized inverifying appropriate size and/or shape of the region of interest in theimage, for example. The type of the diagnostic test may be determinedautomatically (e.g., optical character recognition of branding on theimaged diagnostic test, other distinctive features, machine learning,template matching, etc.) and/or through manual input on a computingdevice (e.g., selected by a user from a displayed, prepopulated list ofdiagnostic tests with known characteristics). In some variations, aproposed diagnostic test type determined through automated methods maythen be manually confirmed or corrected by the user. Furthermore, insome variations, such confirmation or correction may be used to furthertrain a machine learning model (or otherwise refine the automatedmethods described above) to improve its accuracy.

The image (e.g., rough cropped version of the image) may, in somevariations, be further pre-processed prior to (or during) imagevalidation such as that described below. For example, pre-processing mayinclude removing shadows from the image. For example, shadows may beremoved by transforming the image to grayscale to make the imagecolor-agnostic, measuring the mean light level across each axis of theimage and generating a 1D array of light level across each axis, andapplying these 1D arrays along each axis of the image in a manner (e.g.,division) that darkens the lighter areas and lightens the darker areas.

Validating Images

The roughly cropped version of the image may be analyzed through aseries of one or more validation processes to ensure quality of theimage (620). One or more various aspects of the image may be validatedfor quality, including but not limited to lighting level, color balance,exposure level, noise level, image blur level, presence of shadows,and/or presence of glare in the received image. As described in detailbelow, these aspects may be characterized for validating image qualityusing various techniques, such as computer vision techniques. In somevariations, one or more of these aspects may be characterized forvalidating image quality using one or more suitable trained machinelearning models (e.g., deep learning techniques). For example, a machinelearning model may be trained in a supervised manner using training dataincluding images with labeled features (e.g., acceptable or notacceptable orientation of a test, acceptable or not acceptable blurand/or noise level in an image, acceptable or not acceptable level ofexposure, lighting, or color balancing, identified spatial coordinatesof a crop of the image, isolated test or test region relative to thebackground, identified orientation of a test, etc.) that may be used totrain a neural network or other suitable type of machine learningtechnique to identify images of suitable quality with respect to certaincharacteristics. Machine learning models trained in unsupervised orsemi-supervised manners may additionally or alternatively be used tovalidate image quality with respect to one or more aspects of the image.

In some variations, as shown in FIG. 6 , validating quality of the image(620) may include performing any one or more of lighting, saturation,color balance, or illumination checks (622) to ensure there issufficient (or excessive) illumination of the diagnostic test. Forexample, a lighting check may be performed by computing an average(e.g., mean) red-green-blue (RGB) of the image, and then comparing theaverage RGB value of the image to a predetermined threshold conditionsuch as a threshold value or range of values. A suitable threshold valueor range may, for example, be heuristically determined. If the averageRGB value does not satisfy the predetermined threshold condition, thenthe image may be considered to be of insufficient quality, and an errormay be communicated to the user indicating that the image has failedvalidation processes. It should be understood that in other variations,average values of channels in other color spaces (e.g., YUV, CYMK, etc.)may alternatively be used when performing the lighting check describedabove. In other variations the V channel of the HSV color space may beused. Additionally, information provided by the camera or other tools(e.g., BV, lux, ISO, ET, focal length) can be used alone or incombination with each other and/or calculated measurements to determineappropriate illumination.

Additionally or alternatively, validating quality of the image mayinclude performing an exposure check to ensure that the image isproperly exposed. In some variations this can be performed by reviewinginformation provided by the camera or other tools (e.g., BV, lux, ISO,ET, focal length). In some variations, this can be performed byidentifying expected regions of the image and comparing them with eachother to ensure that their relative values are within or outside of aknown heuristically determined range or value, or within or outside of aper-image or region computed range or value. In some variations this mayinclude the above items individually or in combination.

Additionally or alternatively, validating quality of the image (620) mayinclude performing an orientation check (624). In the orientation check,amount of rotation of the diagnostic test within the image may bemeasured, and orientation of the diagnostic test within the image framemay be corrected accordingly. In some variations, known markings on thediagnostic test may function as a “fingerprint” or informative referencefor determining orientation. For example, FIG. 10A depicts an image of adiagnostic test cassette that is rotated within the image frame. Blackprint arrows on the cassette (e.g., arrows adjacent to the assay window)may provide a helpful reference for determining orientation of thediagnostic test in the image. The image may be converted to anothercolor space or a channel of another color space (e.g., grayscale or LABL channel, etc.). A 1D array may be created, where each element of the1D array includes a representative metric (e.g., mean) of pixel valuesfor a region (e.g., column or row) of the image. The 1D array may befiltered (e.g., with a median filter or other appropriate filter) toremove noise and/or other artifacts. The position of peaks in the 1Darray (FIG. 10H) may be considered to correspond to the black printmarkings on the cassette, and may indicate the angle of orientation ofthe assay within the image (or the flipped 180-degree orientation of theassay, though a non-regular spacing between the black print arrows orother asymmetrical characteristic of the markings may help distinguishbetween the actual orientation and the flipped 180-degree orientation).Due to high contrast between the white/light cassette material and theblack print markings on the cassette, the prominence of these peaks isalso expected to be high, which may further help with identification ofthe orientation of the assay. Based on the determined orientation, arotational transformation may be applied to the image to align thediagnostic test to the image frame (FIGS. 10E-10G). As a result, theimage with corrected cardinal orientation of the diagnostic test mayappear to have an expected number of peaks and prominences for the knowntype of diagnostic test (FIG. 10I).

Example variations of methods for performing an orientation check areshown in FIGS. 10B and 10C. For example, as shown in FIGS. 10B and 10C,the image may be converted to a different color space that augments afeature or set of features that are representative of alignment. One ormore computer vision methods or combination of computer vision methodsmay be used to isolate the alignment representative feature(s) andidentify its orientation. For example, suitable computer vision methodsfor isolating such features and identifying orientation include use ofone or more minArea bounding boxes, contour algorithms, etc. As anotherexample, the received image may be cropped (e.g., variations forperforming a rough crop of the image as described above), a contouroperation may be performed on the resulting image. Images havingcontours within an expected region may be accepted, while any imageswith contours outside the expected region may be rejected as poorlyoriented. Additionally or alternatively, one or more suitable computervision techniques may be used to identify the outline of a target imageregion such as the diagnostic test or feature of the test (e.g., testregion), or other targeted predetermined region of the image or test.For example, one or more of edge detection (e.g., Canny, Diriche,Differential, Sobel, Prewitt, Roberts cross, etc.), ridge detection(e.g., Hough transforms, etc.), blob detection (e.g., LoG, DoG, DoH,MSER, PCBR, etc.), feature detection (e.g., affines, SIFT, SURF, GLOH,HOG, etc.), and/or deep learning techniques may be used to determine theoutline of a target region. Thereafter, any in-plane or out-of-planerotations may be identified based on the identified region using aspectratio, area, or perspective transformations, etc. that may calculated ordetermined in order to measure the degree to which the region ismisaligned.

In some variations, an excessively rotated or misaligned image (e.g.,rotation of at least a predetermined threshold of degrees), such as thatdetermined by any of the above-described techniques, may lead to arejection of the image. The user may then be prompted to collect a moreproperly aligned image. Additionally or alternatively, a transformationmay be performed to restore the image to an expected state for furtheranalysis and/or further manual confirmation that the image lookscorrectly oriented.

In some variations, validating quality of the image (620) mayadditionally or alternatively include performing a blur or noise check(626) to determine whether the amount of blur or noise in an image isacceptable. For example, such a check may include optionally convertingthe image into a any one or more of a variety of color spaces andapplying one or more computer vision techniques in various suitablecombinations. Examples of suitable computer vision techniques forreducing blur include Laplacian variance, Laplacian mean, NIQE,Tenengrad, Sobel functions, Fast Fourier Transform FFT), etc. In somevariations, amount of blur or noise may be characterized by generatingan image quality metric for the entire image or a region of interest. Ifthis metric is beyond a predetermined threshold, or a dynamically setthreshold, or if some combination of these values falls beyond acombination of thresholds, the image is treated as having too much blurand/or noise for subsequent analysis. Other methods may include usingsaliency maps or trained deep learning models. Additionally oralternatively, a reference may be used, such as by comparing a sharpenedversion of the image or region with a reference, or a smoothed versionof the image or region with a reference, and/or performing intentionalblurring on the image or region and comparing to a reference, etc. Thereference may include, for example, the original image, a region of theoriginal image, a pre-determined reference image, or a processed imageor region of an image. This comparison results in a metric that can thenbe used as a measurement of the amount of blur or noise in the image.

Additionally or alternatively, in some variations, validating quality ofthe image (620) may include performing a glare check (628) to determinewhether the amount of glare in an image is acceptable. In somevariations, the glare check may include converting the image into a anyone of a variety of color spaces and then applying a variety of computervision techniques in various suitable combinations, such as usingpercentage max values and saliency maps. In some instances, glare maycause saturation of the camera sensor, so values at or near the maximumvalue reported by the sensor may be treated as glare. For example, on ascale from 0 to 255, any pixel with intensity greater than 240 may betreated as potential glare. Furthermore, any pixel having an intensitythat meet a threshold value (dynamically set value or pre-set value) maybe treated as glare. The value may be an absolute value, or may bedefined relative to a maximum intensity in the image, such as withinthree counts from the maximum value in the region of interest.Furthermore, in some cases glare manifests as pixels that are farbrighter than their surroundings. A second dynamic threshold value, suchas the median pixel intensity plus two standard deviations mayadditionally or alternatively be used for determining whether to treat apixel as glare. In some variations, an overall glare level for the imagemay be determined based one or more of these pixel intensity tests. Forexample, pixels meeting one or both of the above-described criteria maybe summed and divided by the total number of pixels in the region ofinterest, to determine a percentage or fraction of pixels that meetcriteria for glare. The fraction of pixels meeting the criteria forglare, divided by the total number of pixels, is reflective of the glarelevel within the region of interest. Other methods for performing aglare check may additionally or alternatively include processing aconverted or non-converted image using a sliding window, and performinga transformation within the window based on features of the windowcompared to the rest of the image in order to isolate regions of glare.

Glare may be present, for example, on the plastic-covered window of atest region (e.g., assay) of a diagnostic test cassette. Accordingly, insome variations to aid the glare check, a crop of the image to isolatethe test region from the rest of the diagnostic test may be performedeither manually (e.g., similar to that described above for a rough cropto isolate the diagnostic test from background) and/or in an automatedmanner. The rough crop may crop out the sample port of the diagnostictest, as well as markings on the cassette (e.g., handwritten ink marks,etc.). As an example of an automated manner of performing a rough cropof the image to isolate the test region, FIG. 11 illustrates a variationof performing a rough crop of the image depicting a diagnostic testincluding a test strip in a cassette. In some variations, assuming thatthe test strip is somewhat centered within an on-screen reticledisplayed on a camera device (e.g., on a mobile computing device), therough crop may remove a predetermined portion (e.g., percentage) of theimage perimeter. For example, the rough crop may remove an outer 30% ofthe image perimeter, though the percentage to be cropped may be anysuitable value.

Additionally or alternatively, the test region may be located using anoutline-based technique. For example, the test region may be locatedbased at least in part on an identified outline of the diagnostic testand estimating location of the test region relative to the outline ofthe diagnostic test. For example, computer vision techniques mayidentify an outline of the diagnostic test (e.g., cassette) usingthresholding, edge finding, blob finding, contour detection, etc. Thelocation of the test region may be determined based on predeterminedcoordinates relative to the outline of the diagnostic test and/or othermarkers, thereby using such known markers in the image as spatiallandmarks. The predetermined coordinates may, for example, be knownafter determining the type of diagnostic test (which may be determinedautomatically and/or manually as described above).

In some variations, a crop of the image to isolate the test region fromthe rest of the image may be performed prior to any of the imagevalidation tests. Furthermore, it should be understood that while FIG. 6depicts image validation processes to be performed in a certain order,the processes such as lighting check (622), orientation check (624),blur/noise check (626), and glare check (628) may be performed in anysuitable order, with any check being omitted or additional checks beinginserted, as tailored based on the determined needs of the test.

Furthermore, in some variations, validation of the image (620) mayutilize information from a configuration file that associated with thetype of diagnostic test being imaged. The type of diagnostic test may bedetermined automatically and/or manually (e.g., as described above) andan associated configuration file may be accessed. Information such asthe size or aspect ratio of the diagnostic test, the number of lines inthe test region, distance between the lines in the test region, thelocation of the test region compared to other landmarks in the test(e.g. top left corner), size of test region (e.g., assay window), etc.may be used to help verify whether the diagnostic test is adequatelyimaged. The configuration file may also contain information such asthresholds, or algorithm controls regarding how and which qualitytechniques are employed and how such techniques are employed.

Additionally, any one or more of the above validation processes may becustomized and/or otherwise tailored for the device(s) performing suchprocesses. Knowledge of device descriptors such as make, model, camerafocal length, and/or other identifying characteristics of the imagingdevice (e.g., smartphone) may be obtained from the operating system, orfrom values predetermined for a custom software build for a specificdevice, etc. The customization of one or more validation processes maybe based at least in part on such device-specific descriptors, andstored as predetermined instructions. The predetermined instructions maybe included in the code for the algorithm, or could be downloaded from aremote location (e.g., server, remote memory device(s), cloud storage,etc.).

Locating Test Region

The test region (e.g., assay window) may be located to proceed withdiagnostic test analysis. The test region may be located in varioussuitable manners. For example, the rough test region isolationtechniques described above (e.g., with reference to FIG. 11 , and/or anoutline-based technique) may be sufficiently precise in some instancesto proceed with analysis. Alternatively, in some variations, as shown inFIG. 6 , after an image has been validated to be of sufficient quality,a more precise or exact crop of the image may be performed (630) to moreprecisely isolate the test region (e.g., assay window) of the diagnostictest. In some variations, as described in detail below, suitablecomputer vision techniques may be used to locate and/or isolate the testregion in the image. Additionally or alternatively, the test region maybe isolated and/or located using one or more suitable trained machinelearning models (e.g., deep learning techniques). For example, a machinelearning model may be trained in a supervised manner using training dataincluding images with labeled test region portions (e.g., assay window,reagent pad, etc.) that may be used to train a neural network or othersuitable type of machine learning technique to identify and/or isolatethe test region in an image. Machine learning models trained inunsupervised or semi-supervised manners may additionally oralternatively be used to locate and/or isolate the test region in animage.

For example, in some variations, the test region may be located based atleast in part on a color-based method. By way of illustration, as shownin FIG. 12A, the cropped image (e.g., rough crop of the test region) maybe converted to the LAB color space or another suitable color space. Theimage array may be split into L, A, and B channel 2D arrays, and a highpercentile value (e.g., 99th, 98th, 97th percentile, etc.) in thehighest contrast channel for the diagnostic test may be calculated amongthe 2D arrays. The highest contrast channel may be selected based on thecolor of the diagnostic test. For example, a high percentile value ofthe A channel array may be determined for a lateral flow immunoassaytest with red/pink lines. Any portions of the image that is not in thepredefined percentile A channel value may be removed via a binary orOtsu filter or the like. In other words, the binary or Otsu filter mayeffectively threshold out all but the brightest A channel values (e.g.,brightest 1%, 2%, 3%, etc. in the A channel) from the image. The largestcontour may be identified in the remaining image, and this contour maybe considered the control line in the test region. As shown in FIG. 12B,the location (e.g., bounds) of the test region may be extrapolated basedon the estimated location of the control line, and/or any otherinformation such as an estimated aspect ratio of the test region (eithernominal value or known from a configuration file associated with thediagnostic test, etc.). Other high contrast channels may be useddepending on the color of the test (e.g., B channel for a diagnostictest that produces blue lines).

Furthermore, the color-based method of locating the test region mayutilize any suitable color space. In some variations, the cropped imagemay be converted to the YUV, XYZ, HSV, CYMK, or other suitable colorspace and the highest contrast color channel in the selected color spacemay be used as described above to identify the control line for testregion extrapolation. For example, the cropped image may be converted tothe YUV color space, and for a diagnostic test with red/pink lines, theV channel may be used to threshold out portions of the image andidentify the control line for test region extrapolation.

As another example, the cropped image may be converted to a grayscaleimage, and the grayscale image may be converted to a 1D array, where theelements of the array are a representative metric (e.g., mean) of aportion such as a row or column of the grayscale image. Peaks may beidentified in the 1D array, and peak(s) with the highest prominence maybe considered the control line as a basis for extrapolation for theentire test region. In the event that more than one high intensity lineor peak are present on the test strip, the peak that is closest to theexpected position of the spatial landmark control line may be determinedto be the spatial landmark. In some instances, multiple control linesmay be present and each control line may be used as a spatial landmarkto refine the location of the test area.

In some instances, the exact test region (e.g., assay window) may bepartially obscured by shadow due to environmental conditions (e.g.,lighting conditions), which may make it more difficult to preciselylocate the test region. For example, in some instances, lightilluminating the diagnostic test from a low angle (e.g., through a floorto ceiling window) may cause the test cassette to cast a shadow onto atleast a portion of the test region. In such instances, a heavy shadowmay be cast onto a first portion of the test region while a secondportion (e.g., remainder) of the test region may be well-illuminated.Accordingly, most of the test region may be found by thresholding (e.g.,binary thresholding, such as that described above) to show only thebrightest areas; however, the darkened or shadowed portion of the testregion is more difficult to find through solely binary thresholding).Thus, in some variations a technique analyzing Laplacian values mayprovide an improved, more accurate method of identifying the test regionin an image, by leveraging the understanding that image regions with ahigh Laplacian value indicate edges of the dark regions of the testregion. Accordingly, adding the “bright” and “dark” areas of the teststrip may result in a combined image that is likely to include theentire test region.

For example, in some variations, a method for identifying a test regionin an image may include determining one or more high-Laplacian regionsof the image that have Laplacian values above a predetermined Laplacianthreshold, determining one or more bright regions of the image that havea brightness above a predetermined brightness threshold, combining theone or more high-Laplacian regions and one or more bright regions of theimage through a bitwise “OR” operation, and defining the test regionbased on the contour of the combined high-Laplacian and bright regionsof the image.

In an exemplary variation of this method, the image may first beconverted to grayscale. The Laplacian of the grayscale image may becalculated, and a suitable binary Laplacian threshold may be applied tothe Laplacian to form a thresholded Laplacian image that includes imageportion(s) having Laplacian values above the Laplacian threshold. TheLaplacian threshold may, for example, be the average (e.g., mean)Laplacian value of the Laplacian image plus the standard deviation inLaplacian image of the Laplacian image. A set of further erosions anddilations may then be applied to the thresholded Laplacian image tosmooth the test region contour. Furthermore, a suitable binary grayscalethreshold may be applied to the grayscale image to form a thresholdedgrayscale image that includes image portion(s) having brighter grayscalevalues above the grayscale threshold. For example, the grayscalethreshold may be the average (e.g., mean) grayscale value of thegrayscale image plus the standard grayscale deviation of the grayscaleimage. Another set of further erosions and dilations may then be appliedto the thresholded grayscale image to smooth the test strip contour. Thethresholded Laplacian image and the thresholded grayscale image may thenmerged with a bitwise “OR” operation. An additional set of furthererosions and dilations may then be applied to the merged image to smooththe test region contour. This merged image may then passed to a contourfinding function. If a contour with the expected (e.g., correct) size,aspect ratio, and position are found, this contour is then marked as thetest region contour and further analysis of the test region may proceedas described elsewhere herein.

In another exemplary variation, the received image may be converted intwo manners to form a grayscale version of the image, as well as anotherversion of the image in a suitable color space such as the HSV colorspace. Similar to the example above, the Laplacian of the grayscaleimage may be calculated, and a suitable binary Laplacian threshold maybe applied to the Laplacian to form a thresholded Laplacian image thatincludes image portion(s) having Laplacian values above the Laplacianthreshold. The Laplacian threshold may, for example, be the average(e.g., mean) Laplacian value of the Laplacian image plus the standarddeviation in Laplacian image of the Laplacian image. A set of furthererosions and dilations may then be applied to the thresholded Laplacianimage to smooth the test region contour. In this example, the V, orvalue, channel of the HSV color space may be used for thresholding toidentify bright regions of the image. For example, a “FOR” loop may beused to iteratively call a thresholding function, where in each cycle ofthis loop, a different cutoff value may be used and a binary thresholdis applied at the cutoff value to form a thresholded value (V) image. Aset of further erosions and dilations may then be applied to smooth thetest strip contour. The thresholded Laplacian image and thresholdedvalue (V) image may then be merged with a bitwise “OR” operation. A setof further erosions and dilations may then be applied to smooth the testregion contour. This merged image may then passed to a contour findingfunction. If a contour with the expected (e.g., correct) size, aspectratio, and position are found, this contour is then marked as the testregion contour and further analysis of the test region may proceed asdescribed elsewhere herein.

In some variations, a diagnostic test may include only one test region(e.g., one assay window), and only one such test region may need to belocated in the image for analysis. However, in some variations, adiagnostic test may include multiple test regions (e.g., multiple assaywindows, or multiple test regions distributed across a single assaywindow), and accordingly in these variations multiple test regions maybe located by repeating the processes described above as appropriate.

In some variations, the test region may additionally or alternatively beisolated by using any number, order, or combination of suitable computervision techniques that isolate the outline of the test region (e.g.,thresholding, edge finding, blob finding, contour detection, etc. and/orother techniques described above). In some variations, a technique forisolating the test region can be aided by first isolating the diagnostictest in the image (e.g., using techniques described previously) tocreate a subset region representing the diagnostic test, and then, usingknown features of the test (e.g., which may be determined from aconfiguration file as described above), and identifying a subset regionin which to search for the test region.

Predicting Test Result

Given a sufficiently cropped image that adequately isolates the testregion(s) of the diagnostic test in the image, a test result may bepredicted (640) based on analyzing the test region depicted in theimage. As shown in FIG. 6 , the precise or exact cropped image may beconverted to grayscale (642 a) or a suitable high contrast color space(642 b), or may be converted to multiple versions including grayscaleand color space versions for further analysis. Furthermore, test linesin the test region may be more easily differentiated from shadows inratiometric color channels such as the LAB “A” or LAB “B” or YUV “V”channels, which are resistant to interference from shadows. Accordingly,in some variations, when determining the position of a line in the testregion, the use of these channels may be preferable to the use of achannel that combines a measure of luminance and color.

In some variations, the cropped test region image may be processed priorto further analysis, in order to remove problematic regions (e.g., rows)that may be misleadingly dark due to dirt, foreign bodies, shadows, etc.For example, the darkest rows of pixels (e.g., as determined in agrayscale version of the image) may be thresholded out based on athreshold cutoff determined by highest percentile value (e.g., 99%, 98%,etc.) in the image or a portion thereof (e.g., by row), based ondetermining peak values of pixel intensity across rows, and/or in anysuitable manner.

FIGS. 13A-13F are schematic illustrations of aspects of an examplevariation of predicting a test result from the imaged test strip. Forexample, starting with a sufficiently cropped image that isolates thetest region of the diagnostic test in the image (FIG. 13A), the imagemay be converted into any suitable high contrast color space such as LABor YUV, etc. (FIG. 13B). The converted image may be split into multiplecolor channels (FIG. 13C). For example, an image in the LAB color spacemay be split into L, A, and B channels. A representative or descriptivestatistic (e.g., mean) may be determined for each column of the highcontrast channel (e.g., A for a diagnostic test with red/pink lines) andcondensed into a 1D array (FIG. 13D). The peaks in this 1D array may belocated (FIG. 13E) and identified as potential lines in the diagnostictest, as every line on the test strip should yield one peak in the 1Darray.

If analyzing the image in grayscale, the grayscale image may becondensed into a 1D array similar to that described above, where theelements of the array are a representative metric (e.g., mean) of aportion such as a row or column of the grayscale image. Similarly, thepeaks in this 1D array may be located and identified as potential linesin the diagnostic test.

Generally, in both color space and grayscale image analysis, therelative position of each peak may be determined, as well as theprominence of each peak. In some variations, the determination of thepresence of a line may be based on any one or more of peak prominence,peak position (e.g., relative to other peaks and/or borders of the testregion), and peak width. In some variations, a control line may beidentified first (e.g., as the strongest presence of a line, based onposition of a peak relative to a test region border, etc.), beforesearching for other peaks/lines that may represent the test result line.If no peaks are found, then the test may be treated as invalid.Similarly, if no control line is found, the test may be treated asinvalid.

As an illustrative example, the identified peaks may be analyzed under aset of rules as shown in Table 1, in a repeated loop manner (e.g., in a“for” loop for all peaks). Expected positions of control lines and/ortest lines, and/or threshold for these lines, may be based oninformation from a configuration file associated with the type ofdiagnostic test being imaged. Alternatively, such positions may beotherwise predetermined, such as with nominal positional values.

In the table below, the noted threshold variables have relative valuesas follows:

-   -   [very_low_threshold]<[slightly_lower_threshold]<[relatively_low_threshold]

TABLE 1 Analysis of peaks in image for prediction of diagnostic testresult Rule # Rule description 1 If no peak has a prominence greaterthan [relatively_low_threshold] => image is invalid. 2 If median LAB orRGB (or other color space) values are outside of an accepted range =>image is invalid. 3 If peak is in a correct position and has aprominence above a position-specific threshold => the peak is added to alist of detected lines. 4 If peak is in a position where a control lineis expected, and no control line has been detected, and peak has athreshold above a predefined threshold or a dynamic threshold thatscales with image characteristics such as light level => treat this asthe control line. 5 If peak is in a position where a strong test line isexpected, and a control line has been detected, and peak has a thresholdabove [slightly_lower_threshold] => treat this as a test line. 6 If peakis in a position where a faint test line is expected, and a control linehas been detected, and peak has a threshold above a predefined thresholdor a dynamic threshold that scales with image characteristics such aslight level => treat this as a test line.

Additionally or alternatively, once the exact location of the testregion is determined (e.g., via any of the techniques described above),intensities of lines in the test region can be measured at expectedpositions in the test region (e.g., known line locations within theassay window for that particular kind of diagnostic test). These lineintensities may be measured in a range of positions in accordance withthe manufacturing tolerances of the test strip for the diagnostic test.For example, if a known manufacturing tolerance for test line locationhas a range of 1 mm (e.g., ±0.5 mm relative to a nominal test lineposition, or ranging between 0.5 mm to the left of the nominal test lineposition and 0.5 mm to the right of the nominal test line position),then line intensity measurements for that test line may be performed atimage locations within that same 1 mm spatial range in the test region.In some variations, the maximum intensity in that 1 mm spatial rangeregion may be used as the representative line intensity for purposes ofanalyzing test result. However, other representative values (e.g., meanline intensity value, average of top quartile of line intensity values,etc.) may be used for purposes of analyzing test result. Furthermore, itshould be understood that a manufacturing tolerance of 1 mm is only anexample for illustrative purposes, and the specific appropriate valuemay vary among test type, diagnostic test brands, etc.

Additionally or alternatively, the test region portion of the image maybe analyzed to predict a test result using one or more suitable trainedmachine learning models (e.g., deep learning techniques). For example, amachine learning model may be trained in a supervised manner usingtraining data including images with labeled test results (e.g., faintpositive, moderately strong positive, strong positive, etc.) for variouskinds of diagnostic tests. This training data may be used to train aneural network or other suitable type of machine learning technique topredict a test result from the image. Machine learning models trained inunsupervised or semi-supervised manners may additionally oralternatively be used to predict a test result from the image.

While the methods are primarily described above with reference toanalyzing lateral flow immunoassay tests, it should be understood thataspects of the methods may also apply to other kinds of diagnostic tests(e.g., colorimetric immunoassay tests). For example, in some variations,analysis of colorimetric immunoassay tests may include identifying atest region (e.g., using fiducials on a scan surface). However, insteadof involving locating control lines and/or test lines, predicting a testresult for a colorimetric test may include locating control vesselsand/or test vessels, and comparing a detected color of a sample in thetest vessel(s) with one or more predetermined colors (e.g., colorreference array) to assess the diagnostic test result. In somevariations, the color-based image analysis may be similar to thatdescribed in U.S. Pat. Nos. 8,655,009 and 8,911,679, each of which isincorporated above.

Furthermore, in some variations for analyzing colorimetric tests, a testregion (e.g., reagent pad) may be located by finding its contours suchas with a suitable contour algorithm. Once the test region is located inthe image, the portion of the image depicting the test region may beconverted to a color space that is best suited for analysis of itscolor. For example, in many cases, the LAB, YUV or CIE-XYZ color spacesare best for analysis of reagent pad color. Once the test region imageportion has been isolated and converted to an appropriate color space,descriptive statistics about its color may be measured. For example, themedian value a descriptive statistic (e.g., value in a color channel inthe color space) of the test region image portion is measured. In somevariations, any areas where glare or foreign objects are detected on thetest region may be disregarded before this calculation of thedescriptive statistic is made. Such glare and/or foreign objects may bedetected as described above, for example.

In some variations present in the scene of any colorimetric test imagemay be reference colors, such as in reference color blocks or othericons printed on a scan surface and depicted in the image). Thesereference color blocks may be located by finding their contours. Oncethe reference color blocks have been located, images of those colorblocks may be converted to a color space that is best suited for theanalysis of their color. Descriptive statistics representing the colorblocks may be calculated similar to that as described above. Thesedescriptive statistics may be used to generate a color correctionmatrix, which can be applied to the entire image or just the region ofinterest. This color correction matrix serves to lessen the impact ofunusual illuminant conditions on measurement of the reagent pad color.

In some variations, the color value of the reagent pad may then betranslated into a reagent concentration and corresponding test result,such as with a lookup table or equation, which may be stored in memoryand accessed at appropriate times.

Communicating the Test Result

After test result(s) have been predicted, they may be output orotherwise communicated to a suitable entity. For example, in somevariations the test result may be communicated to the user through amobile application associated with the diagnostic platform, through anotification message, through email, or in any suitable manner.Additionally or alternatively, the diagnostic test results may becommunicated to a medical care team for the user, such as through anassociated dashboard or other suitable system in communication with thediagnostic platform. Furthermore, in some variations the diagnostic testresults may be communicated to a suitable electronic health record forthe user or other memory storage device.

The diagnostic platform may, in some variations, assist in one or morevarious follow-up actions in view of the predicted test result. Forexample, the diagnostic platform may help the user become connected witha suitable medical care practitioner to discuss questions or options forproceeding with medical care. The diagnostic platform may suggest and/orfacilitate an in-person visit with a medical care practitioner inappropriate. Additionally or alternatively, the diagnostic platform mayassist in providing prescriptions for appropriate medications, providegeneral medical guidance and/or links to resources, and/or othersuitable actions to further the medical care of the user in view of thediagnostic test results.

Example Embodiments

Embodiment 1. A method for analyzing a diagnostic test, the methodcomprising: at one or more processors:

-   -   receiving an image depicting a diagnostic test, wherein the        diagnostic test comprises a test region indicating a test        result;    -   validating quality of the image;    -   locating a test region image portion of the image depicting the        test region of the diagnostic test; and    -   predicting the test result based on the test region image        portion.

Embodiment 2. The method of embodiment 1, wherein validating quality ofthe image comprises assessing at least one of lighting level, colorbalance, noise level, image blur level, presence of shadows, andpresence of glare in the received image.

Embodiment 3. The method of embodiment 1, wherein validating quality ofthe image comprises assessing at least one of location and orientationof the diagnostic test in the received image.

Embodiment 4. The method of embodiment 1, wherein validating quality ofthe image comprises validating imaged quality of one or more controlmarkings in the received image.

Embodiment 5. The method of embodiment 4, wherein the one or morecontrol markings comprise a plurality of lines.

Embodiment 6. The method of embodiment 4, wherein the one or morecontrol markings comprise a plurality of colors.

Embodiment 7. The method of embodiment 1, wherein locating the testregion image portion comprises identifying a boundary of the diagnostictest in the image.

Embodiment 8. The method of embodiment 7, wherein identifying theboundary of the diagnostic test in the image comprises identifying theboundary of the diagnostic test against a high contrast background.

Embodiment 9. The method of embodiment 7, wherein locating the testregion image portion comprises locating the test region image portionbased at least in part on one or more predetermined test regioncoordinates relative to the boundary of the diagnostic test in theimage.

Embodiment 10. The method of embodiment 9, wherein the one or morepredetermined test region coordinates is associated with a type of thediagnostic test.

Embodiment 11. The method of embodiment 1, wherein locating the testregion image portion comprises identifying one or more fiducials on ascan surface in the image and locating the test region image portionbased on the location of the one or more fiducials.

Embodiment 12. The method of embodiment 1, wherein locating the testregion image portion comprises identifying an image portion of interesthaving a peak representative value of a predetermined color channel inthe image, and locating the test region image portion based on thelocation of the largest contour in the predetermined color channel inthe image portion of interest.

Embodiment 13. The method of embodiment 11, wherein the predeterminedcolor channel is in a color space selected from the group consisting ofLAB, YUV, HS V, XYZ, and CYMK.

Embodiment 14. The method of embodiment 1, wherein predicting the testresult comprises identifying one or more peak prominences in apredetermined color channel in the test region image portion.

Embodiment 15. The method of embodiment 14, wherein predicting the testresult comprises evaluating the one or more peak prominences for atleast one of a control and a result indicator in the test region imageportion.

Embodiment 16. The method of embodiment 1, further comprising receivingone or more images of a user and verifying sample collection by the userbased on the one or more images of the user.

Embodiment 17. The method of embodiment 1, further comprisingidentifying a type of the diagnostic test depicted in the receivedimage.

Embodiment 18. The method of embodiment 1, further comprisingcommunicating the predicted test result to a user.

Embodiment 19. The method of embodiment 1, wherein the diagnostic testcomprises a lateral flow immunoassay test.

Embodiment 20. The method of embodiment 19, wherein the lateral flowimmunoassay test is a direct flow immunoassay test.

Embodiment 21. The method of embodiment 1, wherein the diagnostic testcomprises a colorimetric immunoassay test.

Embodiment 22. The method of embodiment 21, wherein the colorimetricimmunoassay test is an isothermal amplification test.

Embodiment 23. A method for facilitating analysis of a diagnostic test,the method comprising:

-   -   at one or more processors:    -   receiving one or more images depicting one or more control        markings on a scan surface, wherein the one or more control        markings are representative of one or more predetermined test        results for the diagnostic test; and    -   verifying detection of the one or more control markings in the        one or more images using at least one computer vision technique.

Embodiment 24. The method of embodiment 23, wherein at least one of theimages depicts the diagnostic test and the one or more control markings.

Embodiment 25. The method of embodiment 23, further comprisingseparately receiving one or more images depicting the diagnostic test.

Embodiment 26. The method of embodiment 23, wherein verifying detectionof the one or more control markings in the one or more images comprisesgenerating an array of a representative value of a series of pixelsassociated with the one or more control markings, determining peaksand/or prominences in the array, and comparing the peaks and/orprominences to one or more predetermined threshold values.

Embodiment 27. The method of embodiment 26, wherein verifying detectionof the one or more control markings comprises treating white space inthe one or more images as a negative control.

Embodiment 28. The method of embodiment 23, further comprisingpredicting a test result of the diagnostic test based on an image of thediagnostic test, in response to detecting the one or more controlmarkings in the one or more images depicting one or more controlmarkings.

Embodiment 29. The method of embodiment 23, further comprising notifyinga user in response to failing to detect the one or more control markingsin the one or more images depicting one or more control markings.

Embodiment 30. The method of embodiment 23, wherein the diagnostic testcomprises a lateral flow immunoassay test, and the one or more controlmarkings comprises one or more lines.

Embodiment 31. The method of embodiment 30, wherein the one or morelines vary in at least one of thickness, color, hue, and reflectivity.

Embodiment 32. The method of embodiment 30, wherein the one or morelines includes black and/or gray lines.

Embodiment 33. The method of embodiment 23, wherein the diagnostic testcomprises a colorimetric immunoassay test, and the one or more controlmarkings comprises one or more colors.

Embodiment 34. The method of embodiment 23, wherein the scan surfacefurther comprises a test placement guide proximate the one or morecontrol markings, wherein the test placement guide is configured toguide placement of the diagnostic test.

Embodiment 35. A system for facilitating analysis of a diagnostic test,the system comprising: a scan surface comprising one or more controlmarkings, wherein the one or more control markings are representative ofone or more predetermined test results for the diagnostic test.

Embodiment 36. The system of embodiment 35, wherein the scan surfacefurther comprises a test placement guide indicating placement of thediagnostic test.

Embodiment 37. The system of embodiment 36, wherein the test placementguide has a color that contrasts with the diagnostic test.

Embodiment 38. The system of embodiment 36, wherein the test placementguide indicates an outline of the diagnostic test.

Embodiment 39. The system of embodiment 35, wherein the diagnostic testcomprises a lateral flow immunoassay test, and the one or more controlmarkings comprises one or more lines.

Embodiment 40. The system of embodiment 39, wherein the one or morelines vary in at least one of thickness, color, hue, and reflectivity.

Embodiment 41. The system of embodiment 39, wherein the one or morelines includes black and/or gray lines.

Embodiment 42. The system of embodiment 35, wherein the diagnostic testcomprises a colorimetric immunoassay test, and the one or more controlmarkings comprises one or more colors.

Embodiment 43. The system of embodiment 35, wherein the scan surfacefurther comprises at least one spatial fiducial.

Embodiment 44. The system of embodiment 35, wherein the scan surfacecomprises at least one computer-readable code with identificationinformation.

Embodiment 45. The system of embodiment 35, wherein the one or morecontrol markings are printed on the scan surface.

Embodiment 46. The system of embodiment 45, wherein the one or morecontrol markings are printed with ink or toner.

Embodiment 47. The system of embodiment 46, wherein the one or morecontrol markings are printed with fluorescent ink.

Embodiment 48. The system of embodiment 47, wherein the fluorescent inkcomprises at least one selected from the group consisting of: europium,rhodamine, fluorescein, alexa fluor, quantum dots, and fluorescentnanoparticles.

Embodiment 49. A diagnostic test kit, comprising:

a diagnostic test comprising a test region for indicating a test result;and

a scan surface comprising one or more control markings, wherein the oneor more control markings are representative of one or more predeterminedtest results for the diagnostic test.

Embodiment 50. The diagnostic test kit of embodiment 49, wherein thediagnostic test comprises a lateral flow immunoassay test, and the oneor more control markings comprises one or more lines.

Embodiment 51. The diagnostic test kit of embodiment 50, wherein the oneor more lines vary in thickness.

Embodiment 52. The diagnostic test kit of embodiment 50, wherein the oneor more lines vary in color.

Embodiment 53. The diagnostic test kit of embodiment 50, wherein the oneor more lines vary in hue.

Embodiment 54. The diagnostic test kid of embodiment 50, wherein the oneor more lines vary in reflectivity.

Embodiment 55. The diagnostic test kit of embodiment 50, wherein the oneor more lines are black and/or gray.

Embodiment 56. The diagnostic test kit of embodiment 50, wherein the oneor more lines are a color that matches an expected color of a testresult line in the diagnostic test.

Embodiment 57. The diagnostic test kit of embodiment 49, wherein thediagnostic test comprises a colorimetric immunoassay test, and the oneor more control markings comprises a plurality of colors.

Embodiment 58. The diagnostic test kit of embodiment 49, wherein the oneor more control markings are printed on the scan surface.

Embodiment 59. The diagnostic test kit of embodiment 58, wherein the oneor more control markings are printed with ink or toner.

Embodiment 60. The diagnostic test kit of embodiment 59, wherein the oneor more control markings are printed with fluorescent ink.

Embodiment 61. The diagnostic test kit of embodiment 60, wherein thefluorescent ink comprises at least one selected from the groupconsisting of: europium, rhodamine, fluorescein, alexa fluor, quantumdots, and fluorescent nanoparticles.

Embodiment 62. The diagnostic test kit of embodiment 49, wherein thescan surface further comprises a test placement guide proximate to theone or more control markings, wherein the test placement guide isconfigured to guide placement of the diagnostic test.

Embodiment 63. The diagnostic test kit of embodiment 49, wherein thescan surface is a first scan surface, and wherein the diagnostic testkit further comprises a second scan surface separate from the first scansurface and comprising a test placement guide.

Embodiment 64. The diagnostic test kit of embodiment 49, furthercomprising a heating device.

Embodiment 65. The diagnostic test kit of embodiment 64, furthercomprising a housing comprising the heating device and a test placementguide proximate the heating device, wherein the test placement guide isconfigured to guide placement of the diagnostic test.

Embodiment 66. The diagnostic test kit of embodiment 65, wherein thescan surface is arranged on the housing.

Embodiment 67. The diagnostic test kit of embodiment 66, wherein thehousing is configured to receive a test sample.

Embodiment 68. The diagnostic test kit of embodiment 67, wherein thehousing comprises a receptacle configured to receive a test samplevessel.

Embodiment 69. The diagnostic test kit of embodiment 68, wherein thereceptacle is proximate the heating device.

The foregoing description, for purposes of explanation, used specificnomenclature to provide a thorough understanding of the invention.However, it will be apparent to one skilled in the art that specificdetails are not required in order to practice the invention. Thus, theforegoing descriptions of specific embodiments of the invention arepresented for purposes of illustration and description. They are notintended to be exhaustive or to limit the invention to the precise formsdisclosed; obviously, many modifications and variations are possible inview of the above teachings. The embodiments were chosen and describedin order to explain the principles of the invention and its practicalapplications, they thereby enable others skilled in the art to utilizethe invention and various embodiments with various modifications as aresuited to the particular use contemplated. It is intended that thefollowing claims and their equivalents define the scope of theinvention.

1-48. (canceled)
 49. A diagnostic test kit, comprising: a diagnostictest comprising a test region for indicating a test result; and a scansurface comprising one or more control markings, wherein the one or morecontrol markings are representative of one or more predetermined testresults for the diagnostic test.
 50. The diagnostic test kit of claim49, wherein the diagnostic test comprises a lateral flow immunoassaytest, and the one or more control markings comprises one or more lines.51. The diagnostic test kit of claim 50, wherein the one or more linesvary in thickness.
 52. The diagnostic test kit of claim 50, wherein theone or more lines vary in color.
 53. The diagnostic test kit of claim50, wherein the one or more lines vary in hue.
 54. The diagnostic testkid of claim 50, wherein the one or more lines vary in reflectivity. 55.The diagnostic test kit of claim 50, wherein the one or more lines areblack and/or gray.
 56. The diagnostic test kit of claim 50, wherein theone or more lines are a color that matches an expected color of a testresult line in the diagnostic test.
 57. The diagnostic test kit of claim49, wherein the diagnostic test comprises a colorimetric immunoassaytest, and the one or more control markings comprises a plurality ofcolors.
 58. The diagnostic test kit of claim 49, wherein the one or morecontrol markings are printed on the scan surface.
 59. The diagnostictest kit of claim 58, wherein the one or more control markings areprinted with ink or toner.
 60. The diagnostic test kit of claim 59,wherein the one or more control markings are printed with fluorescentink.
 61. The diagnostic test kit of claim 60, wherein the fluorescentink comprises at least one selected from the group consisting of:europium, rhodamine, fluorescein, alexa fluor, quantum dots, andfluorescent nanoparticles.
 62. The diagnostic test kit of claim 49,wherein the scan surface further comprises a test placement guideproximate to the one or more control markings, wherein the testplacement guide is configured to guide placement of the diagnostic test.63. The diagnostic test kit of claim 49, wherein the scan surface is afirst scan surface, and wherein the diagnostic test kit furthercomprises a second scan surface separate from the first scan surface andcomprising a test placement guide.
 64. The diagnostic test kit of claim49, further comprising a heating device.
 65. The diagnostic test kit ofclaim 64, further comprising a housing comprising the heating device anda test placement guide proximate the heating device, wherein the testplacement guide is configured to guide placement of the diagnostic test.66. The diagnostic test kit of claim 65, wherein the scan surface isarranged on the housing.
 67. The diagnostic test kit of claim 66,wherein the housing is configured to receive a test sample.
 68. Thediagnostic test kit of claim 67, wherein the housing comprises areceptacle configured to receive a test sample vessel.
 69. Thediagnostic test kit of claim 68, wherein the receptacle is proximate theheating device.